Wafer-map clustering & excursion analysis
Group edge rings, scratch signatures, center clusters, and repeaters into reviewable spatial patterns.
Example outputs shown for illustration. Numbers depend on your samples and protocol.
Image: Illustrative rendering (AI-generated, gpt-image-2), not a production wafer map
What you get
The measurement, today
Defect maps are often interpreted from experience and compared visually. Similar clusters can be labeled differently between shifts, and the associated review images may be scattered across tools.
What it costs
Spatial signatures can focus a process investigation on a chamber, handling path, or consumable. Consistent pattern labels make cross-lot review faster without claiming root cause from the image alone.
From image to reviewed result
- 1
Load the map and images
Bring in wafer-coordinate findings and the review images that support each candidate.
- 2
Cluster the locations
Group defects by spatial proximity, radial position, orientation, and configured signature rules.
- 3
Review the pattern
Present representative images alongside the ring, scratch, center, or repeating pattern candidate.
- 4
Compare lots
Export pattern labels and regional density changes for engineering review.
Scope: Groups visible spatial patterns for review. Pattern similarity is not proof of process root cause; confirm it against equipment history, process data, and electrical test.
Related applications
Wafer defect map & binning
Detect, locate, size, and bin defects across a wafer into a spatial map.
Semiconductor defect patterns
Classify wafer-map patterns and quantify defect clusters and densities.
CMP scratch & planarization inspection
Locate scratches, residual particles, dishing, and erosion signatures after chemical mechanical planarization.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
